Papers › In Defense of the Triplet Loss for Person Re-Identification
In Defense of the Triplet Loss for Person Re-Identification
Alexander Hermans, Lucas Beyer, Bastian Leibe
In the past few years, the field of computer vision has gone through a revolution fueled mainly by the advent of large datasets and the adoption of deep convolutional neural networks for end-to-end learning. The person re-identification subfield is no exception to this. Unfortunately, a prevailing belief in the community seems to be that the triplet loss is inferior to using surrogate losses (classification, verification) followed by a separate metric learning step. We show that, for models trained from scratch as well as pretrained ones, using a variant of the triplet loss to perform end-to-end deep metric learning outperforms most other published methods by a large margin.
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Code
Syntology Ran 6 of 46 code samples harvested from 8 repositories linked to this paper; 40 have no recorded run. Of those that ran: 3 ran · honoured contract; 2 ran · our draft was wrong; 1 ran with no contract checked.
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31 repositories listed; official and paper-mentioned ones first.
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Code Syntology ran Syntology
46 samples harvested; 6 ran; 3 honoured the contract we drafted; 40 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Harvested from 8 repositories linked to this paper, official or community; each sample names its own and says which. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.
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